Results
Contents
Results¶
Data associated with the results presented here can be found at: https://github.com/courtois-neuromod/anat-processing/releases.
Brain¶
This section presents results of brain MRI data. Below are quantitative T1 values computed using the MP2RAGE and the MTsat methods. These values are averaged within the gray matter and white matter masks.
Gray matter qMRI¶
# Python imports
from pathlib import Path
import plotly.graph_objects as go
import plotly.tools as tls
from plotly.offline import plot, iplot, init_notebook_mode
from plotly.validators.scatter.marker import SymbolValidator
from IPython.core.display import display, HTML
import numpy as np
import pandas as pd
# Get data from github repo
dir = Path('neuromod-anat-brain-qmri')
if dir.exists() is False:
!wget -O neuromod-anat-brain-qmri.zip https://github.com/courtois-neuromod/anat-processing/releases/download/r20210726/neuromod-anat-brain-qmri.zip
!unzip -j neuromod-anat-brain-qmri.zip -d ./neuromod-anat-brain-qmri/
/tmp/ipykernel_1863/897671986.py:7: DeprecationWarning:
Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display
# Read data
df = pd.read_csv("./neuromod-anat-brain-qmri/results-neuromod-anat-brain-qmri.csv", converters={'project_id': lambda x: str(x)})
# Sort data acording to subject and session columns
df.sort_values(['subject', 'session'], ascending=[True, True])
# Define lists for metrics
mean_MP2RAGE_t1_matrix = []
mean_MTS_t1_matrix = []
mean_mtr_matrix = []
mean_mtsat_matrix = []
# Get the values for all 4 metric [area, mean_AP, mean_RL, angle_AP]
for i in range(1, 7, 1):
sub_values = df.loc[df['subject'] == i]
mean_MP2RAGE_ses = []
mean_MTS_ses = []
mean_mtr_ses = []
mean_mtsat_ses = []
for j in range(1, 5, 1):
ses_values = sub_values.loc[sub_values['session'] == j]
MEAN_mp2 = -100
MEAN_mts = -100
MEAN_mtr = -100
MEAN_mtsat = -100
for index, row in ses_values.iterrows():
if row['acquisition'] == 'MP2RAGE' and row['metric'] == 'T1map' and row['label'] == 'GM':
MEAN_mp2 = row['mean']
if row['acquisition'] == 'MTS' and row['metric'] == 'T1map' and row['label'] == 'GM':
MEAN_mts = row['mean']
if row['metric'] == 'MTRmap' and row['label'] == 'GM':
MEAN_mtr = row['mean']
if row['metric'] == 'MTsat' and row['label'] == 'GM':
MEAN_mtsat = row['mean']
# Append values to lists for sessions
mean_MP2RAGE_ses.append(MEAN_mp2)
mean_MTS_ses.append(MEAN_mts)
mean_mtr_ses.append(MEAN_mtr)
mean_mtsat_ses.append(MEAN_mtsat)
# Append session lists to main matrices for each metric
mean_MP2RAGE_t1_matrix.append(mean_MP2RAGE_ses)
mean_MTS_t1_matrix.append(mean_MTS_ses)
mean_mtr_matrix.append(mean_mtr_ses)
mean_mtsat_matrix.append(mean_mtsat_ses)
# Add plotly
init_notebook_mode(connected = True)
config={'showLink': False, 'displayModeBar': False}
t1 = [-0.2, -0.06, 0.08, 0.22]
t2 = [0 -0.2 + i*0.14 for i in range(0, 4)]
def get_mean(mean_matrix):
temp = mean_matrix[::]
mean_list = []
for ele in temp:
ele = [i for i in ele if i!=-100]
mean_list.extend(ele)
mean = float('{0:.2f}'.format(np.mean(mean_list)))
return mean
def get_std(mean_matrix):
temp = mean_matrix[::]
mean_list = []
for ele in temp:
ele = [i for i in ele if i!=-100]
mean_list.extend(ele)
std = float('{0:.3f}'.format(np.std(mean_list)))
return std
def get_limit_max(matrix):
temp = matrix[::]
mean_list = []
for ele in temp:
ele = [i for i in ele if i!=-100]
mean_list.extend(ele)
return np.max(mean_list) + (np.max(mean_list)-np.min(mean_list))/4
def get_limit_min(matrix):
temp = matrix[::]
mean_list = []
for ele in temp:
ele = [i for i in ele if i!=-100]
mean_list.extend(ele)
return np.min(mean_list) - (np.max(mean_list)-np.min(mean_list))/4
# Get different symbols (See for reference: https://plotly.com/python/marker-style/)
raw_symbols = SymbolValidator().values
namestems = []
namevariants = []
symbols = []
for i in range(0,len(raw_symbols),3):
name = raw_symbols[i+2]
symbols.append(raw_symbols[i])
namestems.append(name.replace("-open", "").replace("-dot", ""))
namevariants.append(name[len(namestems[-1]):])
# Load fancy colors
tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),
(44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),
(148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),
(227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),
(188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]
# Define labels lists (just in case)
labels =["Session 1", "Session 2","Session 3","Session 4"]
labels_subjects = ['Subject 1', 'Subject 2', 'Subject 3', 'Subject 4', 'Subject 5', 'Subject 6']
labels_int = [i for i in range(1, 7)]
x_rev = labels_int[::-1]
# Def global variables for fonts
x_label_tick_font = 13
y_label_tick_font = 13
general_font = 13
# Add first values for labels [Sub1...Sub6]
figb = go.Figure(data=go.Scatter(x=labels_int,
y=[-1000, -1000, -1000, -1000, -1000, -1000],
mode='markers',
showlegend=False,
marker_color='red'))
# Add MEAN ------ mp2rage ------ T1
for trace in range(0, len(mean_MP2RAGE_t1_matrix)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
if trace == 0:
figb.add_trace(go.Scatter(x=t,
y=mean_MP2RAGE_t1_matrix[trace],
mode='markers',
legendgroup="group1",
hovertemplate =
"Mean : <i> %{y: .2f} </i> sec" +
"<br>" +
"<b>%{text}</b>",
showlegend = True,
text = ['Session {}'.format(i + 1) for i in range(4)],
name= 'T<sub>1</sub> (mp2rage)',
marker_color="rgb"+str(tableau20[0])))
else:
figb.add_trace(go.Scatter(x=t,
y=mean_MP2RAGE_t1_matrix[trace],
mode='markers',
legendgroup="group1",
showlegend = False,
hovertemplate =
"Mean : <i> %{y: .2f} </i> sec" +
"<br>" +
"<b>%{text}</b>",
text = ['Session {}'.format(i + 1) for i in range(4)],
name='T<sub>1</sub> (mp2rage)',
marker_color="rgb"+str(tableau20[0])))
# Add MEAN ------ mts ------ T1
for trace in range(0, len(mean_MTS_t1_matrix)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
if trace == 0:
figb.add_trace(go.Scatter(x=t,
y=mean_MTS_t1_matrix[trace],
mode='markers',
legendgroup="group2",
hovertemplate =
"Mean : <i> %{y: .2f} </i> sec" +
"<br>" +
"<b>%{text}</b>",
showlegend = True,
text = ['Session {}'.format(i + 1) for i in range(4)],
name= 'T<sub>1</sub> (mts)',
marker_symbol=symbols[5],
marker_color="rgb"+str(tableau20[3])))
else:
figb.add_trace(go.Scatter(x=t,
y=mean_MTS_t1_matrix[trace],
mode='markers',
legendgroup="group2",
hovertemplate =
"Mean : <i> %{y: .2f} </i> sec" +
"<br>" +
"<b>%{text}</b>",
showlegend = False,
text = ['Session {}'.format(i + 1) for i in range(4)],
name='T<sup>1</sup> (mts)',
marker_symbol=symbols[5],
marker_color="rgb"+str(tableau20[3])))
# Add MEAN ------ nAn ------ MTR
for trace in range(0, len(mean_mtr_matrix)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
if trace == 0:
figb.add_trace(go.Scatter(x=t,
y=mean_mtr_matrix[trace],
mode='markers',
visible=False,
showlegend = True,
legendgroup="group1",
hovertemplate =
"Mean : <i> %{y: .2f} </i>" +
"<br>" +
"<b>%{text}</b>",
text = ['Session {}'.format(i + 1) for i in range(4)],
name='MTR',
marker_color="rgb"+str(tableau20[0])))
else:
figb.add_trace(go.Scatter(x=t,
y=mean_mtr_matrix[trace],
mode='markers',
visible=False,
showlegend = False,
legendgroup="group1",
hovertemplate =
"Mean : <i> %{y: .2f} </i>" +
"<br>" +
"<b>%{text}</b>",
text = ['Session {}'.format(i + 1) for i in range(4)],
name='MTR',
marker_color="rgb"+str(tableau20[0])))
# Add MEAN ------ nAn ------ MTsat
for trace in range(0, len(mean_mtsat_matrix)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
if trace == 0:
figb.add_trace(go.Scatter(x=t,
y=mean_mtsat_matrix[trace],
mode='markers',
visible=False,
showlegend = True,
legendgroup="group1",
hovertemplate =
"Mean : <i> %{y: .2f} </i>" +
"<br>" +
"<b>%{text}</b>",
text = ['Session {}'.format(i + 1) for i in range(4)],
name='MTsat',
marker_color="rgb"+str(tableau20[0])))
else:
figb.add_trace(go.Scatter(x=t,
y=mean_mtsat_matrix[trace],
mode='markers',
visible=False,
showlegend = False,
legendgroup="group1",
hovertemplate =
"Mean : <i> %{y: .2f} </i>" +
"<br>" +
"<b>%{text}</b>",
text = ['Session {}'.format(i + 1) for i in range(4)],
name='MTsat',
marker_color="rgb"+str(tableau20[0])))
# Calculate means
line_1 = get_mean(mean_MP2RAGE_t1_matrix) # MP2RAGE_t1 --- mean
line_2 = get_mean(mean_MTS_t1_matrix) # MTS_t1 --- mean
line_3 = get_mean(mean_mtr_matrix) # MTR --- mean
line_4 = get_mean(mean_mtsat_matrix) # MTsat --- mean
# Add dotted lines for first button
figb.add_trace(go.Scatter(x=[-1, 0, 1, 2, 3, 4, 5, 6],
y=[line_1]*8,
mode='lines',
name='T<sub>1</sub>(mp2rage) mean',
opacity=0.5,
line=dict(color="rgb"+str(tableau20[0]),
width=2,
dash='dot')))
figb.add_trace(go.Scatter(x=[-1, 0, 1, 2, 3, 4, 5, 6],
y=[line_2]*8,
mode='lines',
name='T<sub>1</sub>(mts) mean',
opacity=0.5,
line=dict(color="rgb"+str(tableau20[3]),
width=2,
dash='dot')))
# Add dotted lines for second button
figb.add_trace(go.Scatter(x=[-1, 0, 1, 2, 3, 4, 5, 6],
y=[line_3]*8,
mode='lines',
visible=False,
name='MTR mean',
opacity=0.5,
line=dict(color="rgb"+str(tableau20[0]),
width=2,
dash='dot')))
# Add dotted lines for thrid button
figb.add_trace(go.Scatter(x=[-1, 0, 1, 2, 3, 4, 5, 6],
y=[line_4]*8,
mode='lines',
visible=False,
name='MTsat mean',
opacity=0.5,
line=dict(color="rgb"+str(tableau20[0]),
width=2,
dash='dot')))
x = [-1, 0, 1, 2, 3, 4, 5, 6]
x_rev = x[::-1]
std_1 = get_std(mean_MP2RAGE_t1_matrix) # MP2RAGE_t1 --- std
std_2 = get_std(mean_MTS_t1_matrix) # MTS_t1 --- std
std_3 = get_std(mean_mtr_matrix) # N/A_mtr --- std
std_4 = get_std(mean_mtsat_matrix) # MTS_mtsat --- std
# Add STD for 1 button
figb.add_trace(go.Scatter(
x=x+x_rev,
y=[line_1+std_1]*8+[line_1-std_1]*8,
fill='toself',
fillcolor='rgba(31, 119, 180,0.15)',
line_color='rgba(255,255,255,0)',
showlegend=False,
hoverinfo='skip',
name='MP2RAGE_t1 STD',
))
figb.add_trace(go.Scatter(
x=x+x_rev,
y=[line_2+std_2]*8+[line_2-std_2]*8,
fill='toself',
fillcolor='rgba(255, 187, 120,0.15)',
line_color='rgba(255,255,255,0)',
showlegend=False,
hoverinfo='skip',
name='MTS_t1 STD',
))
# Add STD for 2 button
figb.add_trace(go.Scatter(
x=x+x_rev,
y=[line_3+std_3]*8+[line_3-std_3]*8,
fill='toself',
visible=False,
fillcolor='rgba(31, 119, 180,0.15)',
line_color='rgba(255,255,255,0)',
showlegend=False,
hoverinfo='skip',
name='MTR STD',
))
# Add STD for 3 button
figb.add_trace(go.Scatter(
x=x+x_rev,
y=[line_4+std_4]*8+[line_4-std_4]*8,
fill='toself',
visible=False,
fillcolor='rgba(31, 119, 180,0.15)',
line_color='rgba(255,255,255,0)',
showlegend=False,
hoverinfo='skip',
name='MTsat STD',
))
figb.update_layout(title = 'Brain gray matter qMRI microstructure',
updatemenus=[
dict(
active = 0,
x=1.23,
y=0.58,
direction="down",
yanchor="top",
buttons=list([
dict(label="T<sub>1</sub>",
method="update",
args=[{"visible": [True] + [True]*12 + [False]*12 + [True]*2 + [False]*2 + [True]*2 + [False]*2},
{"yaxis": dict(range=[get_limit_min(np.append(mean_MP2RAGE_t1_matrix, mean_MTS_t1_matrix, axis=0)), get_limit_max(np.append(mean_MP2RAGE_t1_matrix, mean_MTS_t1_matrix, axis=0))],
title='T<sub>1</sub> [s]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]),
dict(label="MTR",
method="update",
args=[{"visible": [True] + [False]*12 + [True]*6 + [False]*6 + [False]*2 + [True]*1 +[False]*1 + [False]*2 + [True]*1 +[False]*1},
{"yaxis": dict(range=[get_limit_min(mean_mtr_matrix), get_limit_max(mean_mtr_matrix)],
title='MTR [a.u.]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]),
dict(label="MTsat",
method="update",
args=[{"visible": [True] + [False]*18 + [True]*6 + [False]*3 + [True]*1 + [False]*3 + [True]*1},
{"yaxis": dict(range=[get_limit_min(mean_mtsat_matrix),get_limit_max(mean_mtsat_matrix)],
title='MTsat [a.u.]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]) ]) )],
title_x = 0.445,
legend=dict(orientation = 'v',
bordercolor="Gray",
borderwidth=1),
xaxis=dict(range=[-0.45,5.45],
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickvals = [0, 1, 2, 3, 4, 5],
ticktext = labels_subjects,
tickfont = dict(size=x_label_tick_font)),
yaxis_title='T<sub>1</sub> [s]',
yaxis=dict(range=[get_limit_min(np.append(mean_MP2RAGE_t1_matrix, mean_MTS_t1_matrix, axis=0)), get_limit_max(np.append(mean_MP2RAGE_t1_matrix, mean_MTS_t1_matrix, axis=0))],
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font)),
annotations=[
dict(text="Display metric: ",
showarrow=False,
x=1.25,
y=0.62,
xref = 'paper',
yref="paper")],
plot_bgcolor='rgba(227,233,244, 0.5)',
width = 760,
height = 520,
font = dict(size = general_font),
margin=go.layout.Margin(l=50,
r=50,
b=60,
t=35))
# Plot figure
# For jupyter-book rendering --=-- jupyter-lab
plot(figb, filename = 'new-fig-2.html', config = config)
display(HTML('new-fig-2.html'))
# For local jupyter notebook --== binder session
# iplot(figb,config=config)
White matter qMRI¶
Spinal cord¶
Spinal cord cross-sectional area¶
# Python imports
from pathlib import Path
import plotly.graph_objects as go
import plotly.tools as tls
from plotly.offline import plot, iplot, init_notebook_mode
from plotly.validators.scatter.marker import SymbolValidator
from IPython.core.display import display, HTML
import numpy as np
import pandas as pd
# Get data from github repo
dir = Path('spinalcord_results')
if dir.exists() is False:
# Get data from github repo
!wget -O spinalcord_results.zip https://github.com/courtois-neuromod/anat-processing/releases/download/r20210610/spinalcord_results.zip
!unzip -j spinalcord_results.zip -d ./spinalcord_results/
# Add plotly
init_notebook_mode(connected = True)
config={'showLink': False, 'displayModeBar': False}
# Load the data
df_t1 = pd.read_csv("./spinalcord_results/csa-SC_T1w.csv", converters={'project_id': lambda x: str(x)})
df_t2 = pd.read_csv("./spinalcord_results/csa-SC_T2w.csv", converters={'project_id': lambda x: str(x)})
# Insert new columns for Subject and Session and start inserting values
df_t1.insert(0, "Subject", "Any")
df_t1.insert(1, "Session", "Any")
# Get Subject and Session from csv
for index, row in df_t1.iterrows():
subject = int(row['Filename'].split("/")[6].split('-')[1])
session = int(row['Filename'].split("/")[7].split("-")[1])
df_t1.at[index, 'Subject'] = subject
df_t1.at[index, 'Session'] = session
# Sort values based on Subject -- Session
df_2 = df_t1.sort_values(['Subject', 'Session'], ascending=[True, True])
# Define lists for metrics
mean_area_matrix_1 = []
mean_ap_matrix_1 = []
mean_rl_matrix_1 = []
mean_angle_matrix_1 = []
# Get the values for all 4 metric [area, mean_AP, mean_RL, angle_AP]
for i in range(0, 6, 1):
sub_values = df_2.loc[df_2['Subject'] == i+1]
mean_area_ses = []
mean_ap_ses = []
mean_rl_ses = []
mean_angle_ses = []
for j in range(0, 4, 1):
ses_values = sub_values.loc[sub_values['Session'] == j+1]
mean_area = -100
mean_ap = -100
mean_rl = -100
mean_angle = -100
for index, row in ses_values.iterrows():
# Read values
MEAN_area = row['MEAN(area)']
MEAN_diameter_AP = row['MEAN(diameter_AP)']
MEAN_diameter_RL = row['MEAN(diameter_RL)']
MEAN_angle_AP = row['MEAN(angle_AP)']
# Store values
mean_area = MEAN_area
mean_ap = MEAN_diameter_AP
mean_rl = MEAN_diameter_RL
mean_angle = MEAN_angle_AP
# Append values to lists for sessions
mean_area_ses.append(mean_area)
mean_ap_ses.append(mean_ap)
mean_rl_ses.append(mean_rl)
mean_angle_ses.append(mean_angle)
# Append session lists to main matrices for each metric
mean_area_matrix_1.append(mean_area_ses)
mean_ap_matrix_1.append(mean_ap_ses)
mean_rl_matrix_1.append(mean_rl_ses)
mean_angle_matrix_1.append(mean_angle_ses)
# Insert new columns for Subject and Session and start inserting values
df_t2.insert(0, "Subject", "Any")
df_t2.insert(1, "Session", "Any")
# Get Subject and Session from csv
for index, row in df_t2.iterrows():
subject = int(row['Filename'].split("/")[6].split('-')[1])
session = int(row['Filename'].split("/")[7].split("-")[1])
df_t2.at[index, 'Subject'] = subject
df_t2.at[index, 'Session'] = session
# Sort values based on Subject -- Session
df_3 = df_t2.sort_values(['Subject', 'Session'], ascending=[True, True])
# Define lists for metrics
mean_area_matrix_2 = []
mean_ap_matrix_2 = []
mean_rl_matrix_2 = []
mean_angle_matrix_2 = []
# Get the values for all 4 metric [area, mean_AP, mean_RL, angle_AP]
for i in range(0, 6, 1):
sub_values = df_3.loc[df_3['Subject'] == i+1]
mean_area_ses = []
mean_ap_ses = []
mean_rl_ses = []
mean_angle_ses = []
for j in range(0, 4, 1):
ses_values = sub_values.loc[sub_values['Session'] == j+1]
mean_area = -100
mean_ap = -100
mean_rl = -100
mean_angle = -100
for index, row in ses_values.iterrows():
# Read values
MEAN_area = row['MEAN(area)']
MEAN_diameter_AP = row['MEAN(diameter_AP)']
MEAN_diameter_RL = row['MEAN(diameter_RL)']
MEAN_angle_AP = row['MEAN(angle_AP)']
# Store values
mean_area = MEAN_area
mean_ap = MEAN_diameter_AP
mean_rl = MEAN_diameter_RL
mean_angle = MEAN_angle_AP
# Append values to lists for sessions
mean_area_ses.append(mean_area)
mean_ap_ses.append(mean_ap)
mean_rl_ses.append(mean_rl)
mean_angle_ses.append(mean_angle)
# Append session lists to main matrices for each metric
mean_area_matrix_2.append(mean_area_ses)
mean_ap_matrix_2.append(mean_ap_ses)
mean_rl_matrix_2.append(mean_rl_ses)
mean_angle_matrix_2.append(mean_angle_ses)
# Functions for mean and std
def get_mean(mean_matrix):
temp = mean_matrix[::]
mean_list = []
for ele in temp:
ele = [i for i in ele if i!=-100]
mean_list.extend(ele)
mean = float('{0:.7f}'.format(np.mean(mean_list)))
return mean
def get_std(mean_matrix):
temp = mean_matrix[::]
mean_list = []
for ele in temp:
ele = [i for i in ele if i!=-100]
mean_list.extend(ele)
std = float('{0:.7f}'.format(np.std(mean_list)))
return std
# Functions for getting y label limits
def get_limit_max(matrix):
temp = matrix[::]
mean_list = []
for ele in temp:
ele = [i for i in ele if i!=-100]
mean_list.extend(ele)
return np.max(mean_list) + (np.max(mean_list)-np.min(mean_list))/4
def get_limit_min(matrix):
temp = matrix[::]
mean_list = []
for ele in temp:
ele = [i for i in ele if i!=-100]
mean_list.extend(ele)
return np.min(mean_list) - (np.max(mean_list)-np.min(mean_list))/4
# Function for drawing lines
def draw_line(mean_matrix, fig, metric, statistic, visible=False):
# Define x values
x = [-1, 0, 1, 2, 3, 4, 5, 6]
x_rev = x[::-1] # reversed list
x_region = len(x)
if metric == 'T1w' or metric == 'T2w':
if statistic == 'mean':
# Define name and color for mean line
name = 'T<sub>1</sub>w mean' if metric == 'T1w' else 'T<sub>2</sub>w mean'
color = "rgb(31, 119, 180)" if metric == 'T1w' else "rgb(255, 187, 120)"
# Calculate the mean value
line = get_mean(mean_matrix)
# Add the mean for given matrix
fig.add_trace(go.Scatter(x=x,
y=[line]*x_region,
mode='lines',
visible = visible,
name=name,
opacity=0.5,
line=dict(color=color,
width=2,
dash='dot')))
elif statistic == 'std':
# Define name and color for STD region
name = 'T<sub>1</sub>w STD' if metric == 'T1w' else 'T<sub>2</sub>w STD'
fillcolor = 'rgba(31, 119, 180,0.15)' if metric == 'T1w' else 'rgba(255, 187, 120,0.15)'
# Calculate the mean, std value
line = get_mean(mean_matrix)
std = get_std(mean_matrix)
# Add STD for given matrix
fig.add_trace(go.Scatter(
x=x+x_rev,
y=[line+std]*x_region+[line-std]*x_region,
fill='toself',
visible = visible,
fillcolor=fillcolor,
line_color='rgba(255,255,255,0)',
showlegend=False,
hoverinfo='skip',
name=name,
))
else:
if statistic == 'mean':
# Define name and color for mean line
name = metric + " mean"
color = "rgb(31, 119, 180)"
# Calculate the mean value
line = get_mean(mean_matrix)
# Add the mean for given matrix
fig.add_trace(go.Scatter(x=x,
y=[line]*x_region,
mode='lines',
visible = visible,
name=name,
opacity=0.5,
line=dict(color=color,
width=2,
dash='dot')))
elif statistic == 'std':
# Define name and color for STD region
name = metric + " STD"
fillcolor = 'rgba(31, 119, 180,0.15)'
# Calculate the mean, std value
line = get_mean(mean_matrix)
std = get_std(mean_matrix)
# Add STD for given matrix
fig.add_trace(go.Scatter(
x=x+x_rev,
y=[line+std]*x_region+[line-std]*x_region,
fill='toself',
visible = visible,
fillcolor=fillcolor,
line_color='rgba(255,255,255,0)',
showlegend=False,
hoverinfo='skip',
name=name,
))
# Get different symbols (See for reference: https://plotly.com/python/marker-style/)
raw_symbols = SymbolValidator().values
namestems = []
namevariants = []
symbols = []
for i in range(0,len(raw_symbols),3):
name = raw_symbols[i+2]
symbols.append(raw_symbols[i])
namestems.append(name.replace("-open", "").replace("-dot", ""))
namevariants.append(name[len(namestems[-1]):])
# Load fancy colors
tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),
(44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),
(148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),
(227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),
(188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]
# Define lists needed for plotting
t1 = [-0.2, -0.06, 0.08, 0.22]
t2 = [0 -0.2 + i*0.14 for i in range(0, 4)]
labels =["Session 1", "Session 2","Session 3","Session 4"]
labels_subjects = ['Subject 1', 'Subject 2', 'Subject 3', 'Subject 4', 'Subject 5', 'Subject 6']
labels_int = [i for i in range(1, 7)]
x_rev = labels_int[::-1]
# Def global variables for fonts
x_label_tick_font = 13
y_label_tick_font = 13
general_font = 13
# Add first values for labels [Sub1...Sub6]
figb = go.Figure(data=go.Scatter(x=labels_int,
y=[-1000, -1000, -1000, -1000, -1000, -1000],
mode='markers',
showlegend=False,
marker_color='red'))
# Add MEAN [area] --- > T1w
for trace in range(0, len(mean_area_matrix_1)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
showlegend = True if trace ==0 else False
figb.add_trace(go.Scatter(x=t,
y=mean_area_matrix_1[trace],
mode='markers',
legendgroup="group1",
hovertemplate =
"Mean (area): <i> %{y: .2f} </i> mm<sup>2</sup>" +
"<br>" +
"<b>%{text}</b>",
showlegend = showlegend,
text = ['Session {}'.format(i + 1) for i in range(4)],
name= 'T<sub>1</sub>w',
marker_color="rgb"+str(tableau20[0])))
# Add MEAN [area] --- > T2w
for trace in range(0, len(mean_area_matrix_2)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
showlegend = True if trace ==0 else False
figb.add_trace(go.Scatter(x=t,
y=mean_area_matrix_2[trace],
mode='markers',
legendgroup="group2",
hovertemplate =
"Mean (area): <i> %{y: .2f} </i> mm<sup>2</sup>" +
"<br>" +
"<b>%{text}</b>",
showlegend = showlegend,
text = ['Session {}'.format(i + 1) for i in range(4)],
name= 'T<sub>2</sub>w',
marker_symbol=symbols[5],
marker_color="rgb"+str(tableau20[3])))
# Add MEAN [AP] --- > T1w
for trace in range(0, len(mean_ap_matrix_1)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
showlegend = True if trace == 0 else False
figb.add_trace(go.Scatter(x=t,
y=mean_ap_matrix_1[trace],
mode='markers',
visible=False,
showlegend = showlegend,
legendgroup="group1",
hovertemplate =
"Mean [AP]: <i> %{y: .2f} </i> mm" +
"<br>" +
"<b>%{text}</b>",
text = ['Session {}'.format(i + 1) for i in range(4)],
name='T<sub>1</sub>w',
marker_color="rgb"+str(tableau20[0])))
# Add MEAN [AP] --- > T2w
for trace in range(0, len(mean_ap_matrix_2)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
showlegend = True if trace ==0 else False
figb.add_trace(go.Scatter(x=t,
y=mean_ap_matrix_2[trace],
mode='markers',
visible=False,
showlegend = showlegend,
legendgroup="group2",
hovertemplate =
"Mean [AP]: <i> %{y: .2f} </i> mm" +
"<br>" +
"<b>%{text}</b>",
marker_symbol=symbols[5],
text = ['Session {}'.format(i + 1) for i in range(4)],
name='T<sub>2</sub>w',
marker_color="rgb"+str(tableau20[3])))
# Add MEAN [RL] --- > T1w
for trace in range(0, len(mean_rl_matrix_1)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
showlegend = True if trace ==0 else False
figb.add_trace(go.Scatter(x=t,
y=mean_rl_matrix_1[trace],
mode='markers',
visible=False,
showlegend = showlegend,
legendgroup="group1",
hovertemplate =
"Mean (RL): <i> %{y: .2f} </i> mm" +
"<br>" +
"<b>%{text}</b>",
text = ['Session {}'.format(i + 1) for i in range(4)],
name='T<sub>1</sub>w',
marker_color="rgb"+str(tableau20[0])))
# Add MEAN [RL] --- > T2w
for trace in range(0, len(mean_rl_matrix_2)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
showlegend = True if trace ==0 else False
figb.add_trace(go.Scatter(x=t,
y=mean_rl_matrix_2[trace],
mode='markers',
visible=False,
showlegend = showlegend,
legendgroup="group2",
hovertemplate =
"Mean (RL): <i> %{y: .2f} </i> mm" +
"<br>" +
"<b>%{text}</b>",
text = ['Session {}'.format(i + 1) for i in range(4)],
name='T<sub>2</sub>w',
marker_symbol=symbols[5],
marker_color="rgb"+str(tableau20[3])))
# Add MEAN [angle_AP] --- > T1w
for trace in range(0, len(mean_angle_matrix_1)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
showlegend = True if trace ==0 else False
figb.add_trace(go.Scatter(x=t,
y=mean_angle_matrix_1[trace],
mode='markers',
visible=False,
showlegend = showlegend,
legendgroup="group1",
hovertemplate =
"Mean (angle_AP): <i> %{y: .2f} </i>°" +
"<br>" +
"<b>%{text}</b>",
text = ['Session {}'.format(i + 1) for i in range(4)],
name='T<sub>1</sub>w',
marker_color="rgb"+str(tableau20[0])))
# Add MEAN [angle_AP] --- > T2w
for trace in range(0, len(mean_angle_matrix_2)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
showlegend = True if trace ==0 else False
figb.add_trace(go.Scatter(x=t,
y=mean_angle_matrix_2[trace],
mode='markers',
visible=False,
showlegend = showlegend,
legendgroup="group2",
hovertemplate =
"Mean (angle_AP): <i> %{y: .2f} </i>°" +
"<br>" +
"<b>%{text}</b>",
text = ['Session {}'.format(i + 1) for i in range(4)],
name='T<sub>2</sub>w',
marker_symbol=symbols[5],
marker_color="rgb"+str(tableau20[3])))
# Calculate means
draw_line(mean_area_matrix_1, figb, 'T1w', 'mean', visible=True) # T1w mean area --- mean
draw_line(mean_area_matrix_2, figb, 'T2w', 'mean', visible=True) # T2w mean area --- mean
draw_line(mean_ap_matrix_1, figb, 'T1w', 'mean') # T1w mean AP --- mean
draw_line(mean_ap_matrix_2, figb, 'T2w', 'mean') # T2w mean AP --- mean
draw_line(mean_rl_matrix_1, figb, 'T1w', 'mean') # T1w mean RL --- mean
draw_line(mean_rl_matrix_2, figb, 'T2w', 'mean') # T2w mean RL --- mean
draw_line(mean_angle_matrix_1, figb, 'T1w', 'mean') # T1w angle --- mean
draw_line(mean_angle_matrix_2, figb, 'T2w', 'mean') # T2w angle --- mean
draw_line(mean_area_matrix_1, figb, 'T1w', 'std', visible=True) # T1w mean area --- std
draw_line(mean_area_matrix_2, figb, 'T2w', 'std', visible=True) # T2w mean area --- std
draw_line(mean_ap_matrix_1, figb, 'T1w', 'std') # T1w mean AP --- std
draw_line(mean_ap_matrix_2, figb, 'T2w', 'std') # T2w mean AP --- std
draw_line(mean_rl_matrix_1, figb, 'T1w', 'std') # T1w mean RL --- std
draw_line(mean_rl_matrix_2, figb, 'T2w', 'std') # T2w mean RL --- std
draw_line(mean_angle_matrix_1, figb, 'T1w', 'std') # T1w angle --- std
draw_line(mean_angle_matrix_2, figb, 'T2w', 'std') # T2w angle --- std
figb.update_layout(title = ' Spinal cord CSA [T<sub>1</sub>w/T<sub>2</sub>w]',
updatemenus=[
dict(
active = 0,
x=1.27,
y=0.58,
direction="down",
yanchor="top",
buttons=list([
dict(label="Mean (area)",
method="update",
args=[{"visible": [True] + [True]*12 + [False]*36 + [True]*2 + [False]*6 + [True]*2 + [False]*6},
{"yaxis": dict(range=[get_limit_min(np.append(mean_area_matrix_1, mean_area_matrix_2, axis=0)), get_limit_max(np.append(mean_area_matrix_1, mean_area_matrix_2, axis=0))],
title='Mean (area) [mm<sup>2</sup>]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]),
dict(label="Mean (AP)",
method="update",
args=[{"visible": [True] + [False]*12 + [True]*12 + [False]*24 + [False]*2 + [True]*2 +[False]*4 + [False]*2 + [True]*2 +[False]*4},
{"yaxis": dict(range=[get_limit_min(np.append(mean_ap_matrix_1, mean_ap_matrix_2, axis=0)), get_limit_max(np.append(mean_ap_matrix_1, mean_ap_matrix_2, axis=0))],
title='Mean (AP) [mm]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]),
dict(label="Mean (RL)",
method="update",
args=[{"visible": [True] + [False]*24 + [True]*12 + [False]*12 + [False]*4 + [True]*2 +[False]*2 + [False]*4 + [True]*2 +[False]*2},
{"yaxis": dict(range=[get_limit_min(np.append(mean_rl_matrix_1, mean_rl_matrix_2, axis=0)), get_limit_max(np.append(mean_rl_matrix_1, mean_rl_matrix_2, axis=0))],
title='Mean (RL) [mm]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]),
dict(label="Mean (angle)",
method="update",
args=[{"visible": [True] + [False]*36 + [True]*12 + [False]*6 + [True]*2 + [False]*6 + [True]*2 },
{"yaxis": dict(range=[get_limit_min(np.append(mean_angle_matrix_1, mean_angle_matrix_2, axis=0)), get_limit_max(np.append(mean_angle_matrix_1, mean_angle_matrix_2, axis=0))],
title='Mean (angle) [°]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]) ]) )],
title_x = 0.445,
legend=dict(orientation = 'v',
bordercolor="Gray",
borderwidth=1),
xaxis=dict(range=[-0.45,5.45],
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickvals = [0, 1, 2, 3, 4, 5],
ticktext = labels_subjects,
tickfont = dict(size=x_label_tick_font)),
yaxis_title='Mean (area) [mm<sup>2</sup>]',
yaxis=dict(range=[get_limit_min(np.append(mean_area_matrix_1, mean_area_matrix_2, axis=0)), get_limit_max(np.append(mean_area_matrix_1, mean_area_matrix_2, axis=0))],
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font)),
annotations=[
dict(text="Display metric: ",
showarrow=False,
x=1.25,
y=0.62,
xref = 'paper',
yref="paper")],
plot_bgcolor='rgba(227,233,244, 0.5)',
width = 760,
height = 520,
font = dict(size = general_font),
margin=go.layout.Margin(l=50,
r=50,
b=60,
t=35))
# Plot figure
# For jupyter-book rendering --=-- jupyter-lab
plot(figb, filename = 'new-fig.html', config = config)
display(HTML('new-fig.html'))
# For local jupyter notebook --== binder session
# iplot(figb,config=config)
/tmp/ipykernel_1863/3643689153.py:7: DeprecationWarning:
Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display
Gray matter cross-sectional area¶
import plotly.graph_objects as go
import plotly.tools as tls
from plotly.offline import plot, iplot, init_notebook_mode
from plotly.validators.scatter.marker import SymbolValidator
from IPython.core.display import display, HTML
import numpy as np
import pandas as pd
# Add plotly
init_notebook_mode(connected = True)
config={'showLink': False, 'displayModeBar': False}
# Load the data
df_3 = pd.read_csv("./spinalcord_results/csa-GM_T2s.csv", converters={'project_id': lambda x: str(x)})
# Insert new columns for Subject and Session and start inserting values
df_3.insert(0, "Subject", "Any")
df_3.insert(1, "Session", "Any")
# Get Subject and Session from csv
for index, row in df_3.iterrows():
subject = int(row['Filename'].split("/")[6].split('-')[1])
session = int(row['Filename'].split("/")[7].split("-")[1])
df_3.at[index, 'Subject'] = subject
df_3.at[index, 'Session'] = session
# Sort values based on Subject -- Session
df_3 = df_3.sort_values(['Subject', 'Session'], ascending=[True, True])
# Define lists for metrics
mean_area_matrix_1 = []
# Get the values for all 4 metric [area, mean_AP, mean_RL, angle_AP]
for i in range(0, 6, 1):
sub_values = df_3.loc[df_3['Subject'] == i+1]
mean_area_ses = []
for j in range(0, 4, 1):
ses_values = sub_values.loc[sub_values['Session'] == j+1]
mean_area = -100
for index, row in ses_values.iterrows():
# Read values
MEAN_area = row['MEAN(area)']
# Store values
mean_area = MEAN_area
# Append values to lists for sessions
mean_area_ses.append(mean_area)
# Append session lists to main matrices for each metric
mean_area_matrix_1.append(mean_area_ses)
# Add first values for labels [Sub1...Sub6]
figb = go.Figure(data=go.Scatter(x=labels_int,
y=[-1000, -1000, -1000, -1000, -1000, -1000],
mode='markers',
showlegend=False,
marker_color='red'))
# Add MEAN [area] --- > T1w
for trace in range(0, len(mean_area_matrix_1)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
showlegend = True if trace ==0 else False
figb.add_trace(go.Scatter(x=t,
y=mean_area_matrix_1[trace],
mode='markers',
legendgroup="group1",
hovertemplate =
"Mean (area): <i> %{y: .2f} </i> mm<sup>2</sup>" +
"<br>" +
"<b>%{text}</b>",
showlegend = showlegend,
text = ['Session {}'.format(i + 1) for i in range(4)],
name= 'T<sub>2</sub>s',
marker_color="rgb"+str(tableau20[0])))
# Calculate mean and std
draw_line(mean_area_matrix_1, figb, 'T<sub>2</sub>s', 'mean', visible=True) # T2s mean area --- mean
draw_line(mean_area_matrix_1, figb, 'T<sub>2</sub>s', 'std', visible=True) # T2s mean area --- std
figb.update_layout(title = 'Spinal cord gray matter CSA',
title_x = 0.5,
showlegend=False,
xaxis=dict(range=[-0.45,5.45],
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickvals = [0, 1, 2, 3, 4, 5],
ticktext = labels_subjects,
tickfont = dict(size=x_label_tick_font)),
yaxis_title='Mean (area) [mm<sup>2</sup>]',
yaxis=dict(range=[get_limit_min(mean_area_matrix_1), get_limit_max(mean_area_matrix_1)],
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font)),
plot_bgcolor='rgba(227,233,244, 0.5)',
width = 640,
height = 520,
font = dict(size = general_font),
margin=go.layout.Margin(l=50,
r=50,
b=60,
t=35))
# Plot figure
# For jupyter-book rendering --=-- jupyter-lab
plot(figb, filename = 'new-fig-2.html', config = config)
display(HTML('new-fig-2.html'))
# For local jupyter notebook --== binder session
# iplot(figb,config=config)
/tmp/ipykernel_1863/582358489.py:5: DeprecationWarning:
Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display
White matter qMRI¶
import plotly.graph_objects as go
import plotly.tools as tls
from plotly.offline import plot, iplot, init_notebook_mode
from plotly.validators.scatter.marker import SymbolValidator
from IPython.core.display import display, HTML
import numpy as np
import pandas as pd
# Add plotly
init_notebook_mode(connected = True)
config={'showLink': False, 'displayModeBar': False}
# Read data for (DWI_FA, DWI_MD, DWI_RD, MTR, MTsat, T1)
df_dwi_fa = pd.read_csv("./spinalcord_results/DWI_FA.csv", converters={'project_id': lambda x: str(x)})
df_dwi_md = pd.read_csv("./spinalcord_results/DWI_MD.csv", converters={'project_id': lambda x: str(x)})
df_dwi_rd = pd.read_csv("./spinalcord_results/DWI_RD.csv", converters={'project_id': lambda x: str(x)})
df_mtr = pd.read_csv("./spinalcord_results/MTR.csv", converters={'project_id': lambda x: str(x)})
df_mtsat = pd.read_csv("./spinalcord_results/MTsat.csv", converters={'project_id': lambda x: str(x)})
df_t1_2 = pd.read_csv("./spinalcord_results/T1.csv", converters={'project_id': lambda x: str(x)})
# Function for reading session values
def get_sessions_values(df, metric):
'''
:param df: pandas dataframe with results
:param metric: column in dataframe
:return: session values as matrix
'''
df.insert(0, "Subject", "Any")
df.insert(1, "Session", "Any")
# Get Subject and Session from csv
for index, row in df.iterrows():
subject = int(row['Filename'].split("/")[6].split('-')[1])
session = int(row['Filename'].split("/")[7].split("-")[1])
df.at[index, 'Subject'] = subject
df.at[index, 'Session'] = session
# Sort values based on Subject -- Session
df_2 = df.sort_values(['Subject', 'Session'], ascending=[True, True])
# Define lists for metrics
matrix_1 = []
# Get the values for all desired column (metric)
for i in range(0, 6, 1):
sub_values = df_2.loc[df_2['Subject'] == i + 1]
ses = []
for j in range(0, 4, 1):
ses_values = sub_values.loc[sub_values['Session'] == j + 1]
temp = -100
for index, row in ses_values.iterrows():
# Read values
my_value = row[metric]
# Store values
temp = my_value
# Append values to lists for sessions
ses.append(temp)
# Append session lists to main matrices for each metric
matrix_1.append(ses)
return matrix_1
# Get values for sessions, store in lists
matrix_dwi_fa = get_sessions_values(df=df_dwi_fa,metric="WA()")
matrix_dwi_md = get_sessions_values(df=df_dwi_md, metric="WA()")
matrix_dwi_rd = get_sessions_values(df=df_dwi_rd, metric="WA()")
matrix_mtr = get_sessions_values(df=df_mtr, metric="WA()")
matrix_mtsat = get_sessions_values(df=df_mtsat, metric="WA()")
matrix_t1_2 = get_sessions_values(df=df_t1_2, metric="WA()")
# Get different symbols (See for reference: https://plotly.com/python/marker-style/)
raw_symbols = SymbolValidator().values
namestems = []
namevariants = []
symbols = []
for i in range(0,len(raw_symbols),3):
name = raw_symbols[i+2]
symbols.append(raw_symbols[i])
namestems.append(name.replace("-open", "").replace("-dot", ""))
namevariants.append(name[len(namestems[-1]):])
# Load fancy colors
tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),
(44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),
(148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),
(227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),
(188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]
# Define lists needed for plotting
t1 = [-0.2, -0.06, 0.08, 0.22]
t2 = [0 -0.2 + i*0.14 for i in range(0, 4)]
labels =["Session 1", "Session 2","Session 3","Session 4"]
labels_subjects = ['Subject 1', 'Subject 2', 'Subject 3', 'Subject 4', 'Subject 5', 'Subject 6']
labels_int = [i for i in range(1, 7)]
x_rev = labels_int[::-1]
# Add first values for labels [Sub1...Sub6]
figb = go.Figure(data=go.Scatter(x=labels_int,
y=[-1000, -1000, -1000, -1000, -1000, -1000],
mode='markers',
showlegend=False,
marker_color='red'))
# Add DWI_FA
for trace in range(0, len(matrix_dwi_fa)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
showlegend = True if trace ==0 else False
figb.add_trace(go.Scatter(x=t,
y=matrix_dwi_fa[trace],
mode='markers',
legendgroup="group1",
hovertemplate =
"<i> %{y: .2f} </i>" +
"<br>" +
"<b>%{text}</b>",
showlegend = showlegend,
text = ['Session {}'.format(i + 1) for i in range(4)],
name= 'DWI_FA',
marker_color="rgb"+str(tableau20[0])))
# Add DWI_MD
for trace in range(0, len(matrix_dwi_md)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
showlegend = True if trace ==0 else False
figb.add_trace(go.Scatter(x=t,
y=matrix_dwi_md[trace],
mode='markers',
visible=False,
legendgroup="group1",
hovertemplate =
"<i> %{y: .6f} </i> mm<sup>2</sup>/s" +
"<br>" +
"<b>%{text}</b>",
showlegend = showlegend,
text = ['Session {}'.format(i + 1) for i in range(4)],
name= 'DWI_MD',
marker_color="rgb"+str(tableau20[0])))
# Add DWI_RD
for trace in range(0, len(matrix_dwi_rd)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
showlegend = True if trace ==0 else False
figb.add_trace(go.Scatter(x=t,
y=matrix_dwi_rd[trace],
mode='markers',
visible=False,
legendgroup="group1",
hovertemplate =
"<i> %{y: .6f} </i> mm<sup>2</sup>/s" +
"<br>" +
"<b>%{text}</b>",
showlegend = showlegend,
text = ['Session {}'.format(i + 1) for i in range(4)],
name= 'DWI_RD',
marker_color="rgb"+str(tableau20[0])))
# Add MTR
for trace in range(0, len(matrix_mtr)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
showlegend = True if trace ==0 else False
figb.add_trace(go.Scatter(x=t,
y=matrix_mtr[trace],
mode='markers',
visible=False,
legendgroup="group1",
hovertemplate =
"<i> %{y: .2f} </i>" +
"<br>" +
"<b>%{text}</b>",
showlegend = showlegend,
text = ['Session {}'.format(i + 1) for i in range(4)],
name= 'MTR',
marker_color="rgb"+str(tableau20[0])))
# Add MTsat
for trace in range(0, len(matrix_mtsat)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
showlegend = True if trace ==0 else False
figb.add_trace(go.Scatter(x=t,
y=matrix_mtsat[trace],
mode='markers',
visible=False,
legendgroup="group1",
hovertemplate =
"<i> %{y: .2f} </i>" +
"<br>" +
"<b>%{text}</b>",
showlegend = showlegend,
text = ['Session {}'.format(i + 1) for i in range(4)],
name= 'MTsat',
marker_color="rgb"+str(tableau20[0])))
# Add T1
for trace in range(0, len(matrix_t1_2)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
showlegend = True if trace ==0 else False
figb.add_trace(go.Scatter(x=t,
y=matrix_t1_2[trace],
mode='markers',
visible=False,
legendgroup="group1",
hovertemplate =
"<i> %{y: .7f} </i> sec" +
"<br>" +
"<b>%{text}</b>",
showlegend = showlegend,
text = ['Session {}'.format(i + 1) for i in range(4)],
name= 'T<sub>1</sub>',
marker_color="rgb"+str(tableau20[0])))
# Calculate means
draw_line(matrix_dwi_fa, figb, 'DWI_FA', 'mean', visible=True) # DWI_FA --- mean
draw_line(matrix_dwi_md, figb, 'DWI_MD', 'mean') # DWI_MD --- mean
draw_line(matrix_dwi_rd, figb, 'DWI_RD', 'mean') # DWI_RD --- mean
draw_line(matrix_mtr, figb, 'MTR', 'mean') # MTR --- mean
draw_line(matrix_mtsat, figb, 'MTsat', 'mean') # MTsat --- mean
draw_line(matrix_t1_2, figb, 'T<sub>1</sub>', 'mean') # T1 --- mean
# Calculate stds
draw_line(matrix_dwi_fa, figb, 'DWI_FA', 'std', visible=True) # DWI_FA --- mean
draw_line(matrix_dwi_md, figb, 'DWI_MD', 'std') # DWI_MD --- mean
draw_line(matrix_dwi_rd, figb, 'DWI_RD', 'std') # DWI_RD --- mean
draw_line(matrix_mtr, figb, 'MTR', 'std') # MTR --- mean
draw_line(matrix_mtsat, figb, 'MTsat', 'std') # MTsat --- mean
draw_line(matrix_t1_2, figb, 'T<sub>1</sub>', 'std') # T1 --- mean
figb.update_layout(title = 'Spinal cord qMRI microstructure ',
updatemenus=[
dict(
active = 0,
x=1.24,
y=0.58,
direction="down",
yanchor="top",
buttons=list([
dict(label="DWI_FA",
method="update",
args=[{"visible": [True] + [True]*6 + [False]*30 + [True]*1 + [False]*5 + [True]*1 + [False]*5},
{"yaxis": dict(range=[get_limit_min(matrix_dwi_fa), get_limit_max(matrix_dwi_fa)],
title='DWI_FA [a.u.]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]),
dict(label="DWI_MD",
method="update",
args=[{"visible": [True] + [False]*6 + [True]*6 + [False]*24 + [False]*1 + [True]*1 +[False]*4 + [False]*1 + [True]*1 +[False]*4},
{"yaxis": dict(range=[get_limit_min(matrix_dwi_md), get_limit_max(matrix_dwi_md)],
title='DWI_MD [mm<sup>2</sup>/s]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]),
dict(label="DWI_RD",
method="update",
args=[{"visible": [True] + [False]*12 + [True]*6 + [False]*18 + [False]*2 + [True]*1 +[False]*3 + [False]*2 + [True]*1 +[False]*3},
{"yaxis": dict(range=[get_limit_min(matrix_dwi_rd), get_limit_max(matrix_dwi_rd)],
title=' DWI_RD [mm<sup>2</sup>/s]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]),
dict(label="MTR",
method="update",
args=[{"visible": [True] + [False]*18 + [True]*6 + [False]*12 + [False]*3 + [True]*1 +[False]*2 + [False]*3 + [True]*1 +[False]*2},
{"yaxis": dict(range=[get_limit_min(matrix_mtr), get_limit_max(matrix_mtr)],
title='MTR [a.u.]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]),
dict(label="MTsat",
method="update",
args=[{"visible": [True] + [False]*24 + [True]*6 + [False]*6 + [False]*4 + [True]*1 +[False]*1 + [False]*4 + [True]*1 +[False]*1},
{"yaxis": dict(range=[get_limit_min(matrix_mtsat), get_limit_max(matrix_mtsat)],
title='MTsat [a.u.]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]),
dict(label="T<sub>1</sub>",
method="update",
args=[{"visible": [True] + [False]*30 + [True]*6 + [False]*5 + [True]*1 + [False]*5 + [True]*1 },
{"yaxis": dict(range=[get_limit_min(matrix_t1_2), get_limit_max(matrix_t1_2)],
title='T<sub>1</sub> [s]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]) ]) )],
title_x = 0.445,
legend=dict(orientation = 'v',
bordercolor="Gray",
borderwidth=1),
xaxis=dict(range=[-0.45,5.45],
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickvals = [0, 1, 2, 3, 4, 5],
ticktext = labels_subjects,
tickfont = dict(size=x_label_tick_font)),
yaxis_title='DWI_FA [a.u.]',
yaxis=dict(range=[get_limit_min(matrix_dwi_fa), get_limit_max(matrix_dwi_fa)],
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font)),
annotations=[
dict(text="Display metric: ",
showarrow=False,
x=1.25,
y=0.62,
xref = 'paper',
yref="paper")],
plot_bgcolor='rgba(227,233,244, 0.5)',
width = 760,
height = 520,
font = dict(size = general_font),
margin=go.layout.Margin(l=50,
r=50,
b=60,
t=35))
# Plot figure
# For jupyter-book rendering --=-- jupyter-lab
plot(figb, filename = 'new-fig-3.html', config = config)
display(HTML('new-fig-3.html'))
# For local jupyter notebook --== binder session
# iplot(figb,config=config)
/tmp/ipykernel_1863/3287582139.py:5: DeprecationWarning:
Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display
import plotly.graph_objects as go
import plotly.tools as tls
from plotly.offline import plot, iplot, init_notebook_mode
from plotly.validators.scatter.marker import SymbolValidator
from IPython.core.display import display, HTML
import numpy as np
import pandas as pd
# Read data
df = pd.read_csv("./neuromod-anat-brain-qmri/results-neuromod-anat-brain-qmri.csv", converters={'project_id': lambda x: str(x)})
# Sort data acording to subject and session columns
df.sort_values(['subject', 'session'], ascending=[True, True])
# Define lists for metrics
mean_MP2RAGE_t1_matrix = []
mean_MTS_t1_matrix = []
mean_mtr_matrix = []
mean_mtsat_matrix = []
# Get the values for all 4 metric [area, mean_AP, mean_RL, angle_AP]
for i in range(1, 7, 1):
sub_values = df.loc[df['subject'] == i]
mean_MP2RAGE_ses = []
mean_MTS_ses = []
mean_mtr_ses = []
mean_mtsat_ses = []
for j in range(1, 5, 1):
ses_values = sub_values.loc[sub_values['session'] == j]
MEAN_mp2 = -100
MEAN_mts = -100
MEAN_mtr = -100
MEAN_mtsat = -100
for index, row in ses_values.iterrows():
if row['acquisition'] == 'MP2RAGE' and row['metric'] == 'T1map' and row['label'] == 'WM':
MEAN_mp2 = row['mean']
if row['acquisition'] == 'MTS' and row['metric'] == 'T1map' and row['label'] == 'WM':
MEAN_mts = row['mean']
if row['metric'] == 'MTRmap' and row['label'] == 'WM':
MEAN_mtr = row['mean']
if row['metric'] == 'MTsat' and row['label'] == 'WM':
MEAN_mtsat = row['mean']
# Append values to lists for sessions
mean_MP2RAGE_ses.append(MEAN_mp2)
mean_MTS_ses.append(MEAN_mts)
mean_mtr_ses.append(MEAN_mtr)
mean_mtsat_ses.append(MEAN_mtsat)
# Append session lists to main matrices for each metric
mean_MP2RAGE_t1_matrix.append(mean_MP2RAGE_ses)
mean_MTS_t1_matrix.append(mean_MTS_ses)
mean_mtr_matrix.append(mean_mtr_ses)
mean_mtsat_matrix.append(mean_mtsat_ses)
# Add plotly
init_notebook_mode(connected = True)
config={'showLink': False, 'displayModeBar': False}
t1 = [-0.2, -0.06, 0.08, 0.22]
t2 = [0 -0.2 + i*0.14 for i in range(0, 4)]
def get_mean(mean_matrix):
temp = mean_matrix[::]
mean_list = []
for ele in temp:
ele = [i for i in ele if i!=-100]
mean_list.extend(ele)
mean = float('{0:.2f}'.format(np.mean(mean_list)))
return mean
def get_std(mean_matrix):
temp = mean_matrix[::]
mean_list = []
for ele in temp:
ele = [i for i in ele if i!=-100]
mean_list.extend(ele)
std = float('{0:.3f}'.format(np.std(mean_list)))
return std
# Get different symbols (See for reference: https://plotly.com/python/marker-style/)
raw_symbols = SymbolValidator().values
namestems = []
namevariants = []
symbols = []
for i in range(0,len(raw_symbols),3):
name = raw_symbols[i+2]
symbols.append(raw_symbols[i])
namestems.append(name.replace("-open", "").replace("-dot", ""))
namevariants.append(name[len(namestems[-1]):])
# Load fancy colors
tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),
(44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),
(148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),
(227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),
(188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]
# Define labels lists (just in case)
labels =["Session 1", "Session 2","Session 3","Session 4"]
labels_subjects = ['Subject 1', 'Subject 2', 'Subject 3', 'Subject 4', 'Subject 5', 'Subject 6']
labels_int = [i for i in range(1, 7)]
x_rev = labels_int[::-1]
# Def global variables for font sizes
x_label_tick_font = 13
y_label_tick_font = 13
general_font = 13
# Add first values for labels [Sub1...Sub6]
figb = go.Figure(data=go.Scatter(x=labels_int,
y=[-1000, -1000, -1000, -1000, -1000, -1000],
mode='markers',
showlegend=False,
marker_color='red'))
# Add MEAN ------ mp2rage ------ T1
for trace in range(0, len(mean_MP2RAGE_t1_matrix)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
if trace == 0:
figb.add_trace(go.Scatter(x=t,
y=mean_MP2RAGE_t1_matrix[trace],
mode='markers',
legendgroup="group1",
hovertemplate =
"Mean : <i> %{y: .2f} </i> sec" +
"<br>" +
"<b>%{text}</b>",
showlegend = True,
text = ['Session {}'.format(i + 1) for i in range(4)],
name= 'T<sub>1</sub> (mp2rage)',
marker_color="rgb"+str(tableau20[0])))
else:
figb.add_trace(go.Scatter(x=t,
y=mean_MP2RAGE_t1_matrix[trace],
mode='markers',
legendgroup="group1",
showlegend = False,
hovertemplate =
"Mean : <i> %{y: .2f} </i> sec" +
"<br>" +
"<b>%{text}</b>",
text = ['Session {}'.format(i + 1) for i in range(4)],
name='T<sub>1</sub> (mp2rage)',
marker_color="rgb"+str(tableau20[0])))
# Add MEAN ------ mts ------ T1
for trace in range(0, len(mean_MTS_t1_matrix)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
if trace == 0:
figb.add_trace(go.Scatter(x=t,
y=mean_MTS_t1_matrix[trace],
mode='markers',
legendgroup="group2",
hovertemplate =
"Mean : <i> %{y: .2f} </i> sec" +
"<br>" +
"<b>%{text}</b>",
showlegend = True,
text = ['Session {}'.format(i + 1) for i in range(4)],
name= 'T<sub>1</sub> (mts)',
marker_symbol=symbols[5],
marker_color="rgb"+str(tableau20[3])))
else:
figb.add_trace(go.Scatter(x=t,
y=mean_MTS_t1_matrix[trace],
mode='markers',
legendgroup="group2",
hovertemplate =
"Mean : <i> %{y: .2f} </i> sec" +
"<br>" +
"<b>%{text}</b>",
showlegend = False,
text = ['Session {}'.format(i + 1) for i in range(4)],
name='T<sup>1</sup> (mts)',
marker_symbol=symbols[5],
marker_color="rgb"+str(tableau20[3])))
# Add MEAN ------ nAn ------ MTR
for trace in range(0, len(mean_mtr_matrix)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
if trace == 0:
figb.add_trace(go.Scatter(x=t,
y=mean_mtr_matrix[trace],
mode='markers',
visible=False,
showlegend = True,
legendgroup="group1",
hovertemplate =
"Mean : <i> %{y: .2f} </i>" +
"<br>" +
"<b>%{text}</b>",
text = ['Session {}'.format(i + 1) for i in range(4)],
name='MTR',
marker_color="rgb"+str(tableau20[0])))
else:
figb.add_trace(go.Scatter(x=t,
y=mean_mtr_matrix[trace],
mode='markers',
visible=False,
showlegend = False,
legendgroup="group1",
hovertemplate =
"Mean : <i> %{y: .2f} </i>" +
"<br>" +
"<b>%{text}</b>",
text = ['Session {}'.format(i + 1) for i in range(4)],
name='MTR',
marker_color="rgb"+str(tableau20[0])))
# Add MEAN ------ nAn ------ MTsat
for trace in range(0, len(mean_mtsat_matrix)):
t = [trace -0.2 + i*0.14 for i in range(0, 4)]
if trace == 0:
figb.add_trace(go.Scatter(x=t,
y=mean_mtsat_matrix[trace],
mode='markers',
visible=False,
showlegend = True,
legendgroup="group1",
hovertemplate =
"Mean : <i> %{y: .2f} </i>" +
"<br>" +
"<b>%{text}</b>",
text = ['Session {}'.format(i + 1) for i in range(4)],
name='MTsat',
marker_color="rgb"+str(tableau20[0])))
else:
figb.add_trace(go.Scatter(x=t,
y=mean_mtsat_matrix[trace],
mode='markers',
visible=False,
showlegend = False,
legendgroup="group1",
hovertemplate =
"Mean : <i> %{y: .2f} </i>" +
"<br>" +
"<b>%{text}</b>",
text = ['Session {}'.format(i + 1) for i in range(4)],
name='MTsat',
marker_color="rgb"+str(tableau20[0])))
# Calculate means
line_1 = get_mean(mean_MP2RAGE_t1_matrix) # MP2RAGE_t1 --- mean
line_2 = get_mean(mean_MTS_t1_matrix) # MTS_t1 --- mean
line_3 = get_mean(mean_mtr_matrix) # MTR --- mean
line_4 = get_mean(mean_mtsat_matrix) # MTsat --- mean
# Add dotted lines for first button
figb.add_trace(go.Scatter(x=[-1, 0, 1, 2, 3, 4, 5, 6],
y=[line_1]*8,
mode='lines',
name='T<sub>1</sub>(mp2rage) mean',
opacity=0.5,
line=dict(color="rgb"+str(tableau20[0]),
width=2,
dash='dot')))
figb.add_trace(go.Scatter(x=[-1, 0, 1, 2, 3, 4, 5, 6],
y=[line_2]*8,
mode='lines',
name='T<sub>1</sub>(mts) mean',
opacity=0.5,
line=dict(color="rgb"+str(tableau20[3]),
width=2,
dash='dot')))
# Add dotted lines for second button
figb.add_trace(go.Scatter(x=[-1, 0, 1, 2, 3, 4, 5, 6],
y=[line_3]*8,
mode='lines',
visible=False,
name='MTR mean',
opacity=0.5,
line=dict(color="rgb"+str(tableau20[0]),
width=2,
dash='dot')))
# Add dotted lines for thrid button
figb.add_trace(go.Scatter(x=[-1, 0, 1, 2, 3, 4, 5, 6],
y=[line_4]*8,
mode='lines',
visible=False,
name='MTsat mean',
opacity=0.5,
line=dict(color="rgb"+str(tableau20[0]),
width=2,
dash='dot')))
x = [-1, 0, 1, 2, 3, 4, 5, 6]
x_rev = x[::-1]
std_1 = get_std(mean_MP2RAGE_t1_matrix) # MP2RAGE_t1 --- std
std_2 = get_std(mean_MTS_t1_matrix) # MTS_t1 --- std
std_3 = get_std(mean_mtr_matrix) # N/A_mtr --- std
std_4 = get_std(mean_mtsat_matrix) # MTS_mtsat --- std
# Add STD for 1 button
figb.add_trace(go.Scatter(
x=x+x_rev,
y=[line_1+std_1]*8+[line_1-std_1]*8,
fill='toself',
fillcolor='rgba(31, 119, 180,0.15)',
line_color='rgba(255,255,255,0)',
showlegend=False,
hoverinfo='skip',
name='MP2RAGE_t1 STD',
))
figb.add_trace(go.Scatter(
x=x+x_rev,
y=[line_2+std_2]*8+[line_2-std_2]*8,
fill='toself',
fillcolor='rgba(255, 187, 120,0.15)',
line_color='rgba(255,255,255,0)',
showlegend=False,
hoverinfo='skip',
name='MTS_t1 STD',
))
# Add STD for 2 button
figb.add_trace(go.Scatter(
x=x+x_rev,
y=[line_3+std_3]*8+[line_3-std_3]*8,
fill='toself',
visible=False,
fillcolor='rgba(31, 119, 180,0.15)',
line_color='rgba(255,255,255,0)',
showlegend=False,
hoverinfo='skip',
name='MTR STD',
))
# Add STD for 3 button
figb.add_trace(go.Scatter(
x=x+x_rev,
y=[line_4+std_4]*8+[line_4-std_4]*8,
fill='toself',
visible=False,
fillcolor='rgba(31, 119, 180,0.15)',
line_color='rgba(255,255,255,0)',
showlegend=False,
hoverinfo='skip',
name='MTsat STD',
))
figb.update_layout(title = 'Brain white matter qMRI microstructure',
updatemenus=[
dict(
active = 0,
x=1.23,
y=0.58,
direction="down",
yanchor="top",
buttons=list([
dict(label="T<sub>1</sub>",
method="update",
args=[{"visible": [True] + [True]*12 + [False]*12 + [True]*2 + [False]*2 + [True]*2 + [False]*2},
{"yaxis": dict(range=[get_limit_min(np.append(mean_MP2RAGE_t1_matrix, mean_MTS_t1_matrix, axis=0)), get_limit_max(np.append(mean_MP2RAGE_t1_matrix, mean_MTS_t1_matrix, axis=0))],
title='T<sub>1</sub> [s]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]),
dict(label="MTR",
method="update",
args=[{"visible": [True] + [False]*12 + [True]*6 + [False]*6 + [False]*2 + [True]*1 +[False]*1 + [False]*2 + [True]*1 +[False]*1},
{"yaxis": dict(range=[get_limit_min(mean_mtr_matrix), get_limit_max(mean_mtr_matrix)],
title='MTR [a.u.]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]),
dict(label="MTsat",
method="update",
args=[{"visible": [True] + [False]*18 + [True]*6 + [False]*3 + [True]*1 + [False]*3 + [True]*1},
{"yaxis": dict(range=[get_limit_min(mean_mtsat_matrix),get_limit_max(mean_mtsat_matrix)],
title='MTsat [a.u.]',
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font))}]) ]) )],
title_x = 0.445,
legend=dict(orientation = 'v',
bordercolor="Gray",
borderwidth=1),
xaxis=dict(range=[-0.45,5.45],
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickvals = [0, 1, 2, 3, 4, 5],
ticktext = labels_subjects,
tickfont = dict(size=x_label_tick_font)),
yaxis_title='T<sub>1</sub> [s]',
yaxis=dict(range=[get_limit_min(np.append(mean_MP2RAGE_t1_matrix, mean_MTS_t1_matrix, axis=0)), get_limit_max(np.append(mean_MP2RAGE_t1_matrix, mean_MTS_t1_matrix, axis=0))],
mirror=True,
ticks='outside',
showline=True,
linecolor='#000',
tickfont = dict(size=y_label_tick_font)),
annotations=[
dict(text="Display metric: ",
showarrow=False,
x=1.25,
y=0.62,
xref = 'paper',
yref="paper")],
plot_bgcolor='rgba(227,233,244, 0.5)',
width = 760,
height = 520,
font = dict(size = general_font),
margin=go.layout.Margin(l=50,
r=50,
b=60,
t=35))
# Plot figure
# For jupyter-book rendering --=-- jupyter-lab
plot(figb, filename = 'new-fig-4.html', config = config)
display(HTML('new-fig-4.html'))
# For local jupyter notebook --== binder session
# iplot(figb,config=config)
/tmp/ipykernel_1863/3892547858.py:5: DeprecationWarning:
Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display